Semiparametric quantile models for ascending auctions with asymmetric bidders
Semiparametric quantile models for ascending auctions with asymmetric bidders
The paper proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon (2008), this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is in two stage. The asymmetry parameters are estimated from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes (2017). Specification testing procedures are also considered. A timber application reveals that weaker bidders have 30% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer (1996) valid under symmetry.
Private values, ascending auctions, asymmetry, quantile regression, seller expected revenue, two stage quantile regression estimation
Bhattacharya, Jayeeta
1574428c-484f-48c6-90cf-644cfad7716d
Guerre, Emmanuel
cbfd8f6b-2820-4cb5-b4f6-48a55f81e278
Gimenes, Nathalie
05e081b0-b137-40f4-94b2-268f3832ae7c
1 March 2021
Bhattacharya, Jayeeta
1574428c-484f-48c6-90cf-644cfad7716d
Guerre, Emmanuel
cbfd8f6b-2820-4cb5-b4f6-48a55f81e278
Gimenes, Nathalie
05e081b0-b137-40f4-94b2-268f3832ae7c
Bhattacharya, Jayeeta, Guerre, Emmanuel and Gimenes, Nathalie
(2021)
Semiparametric quantile models for ascending auctions with asymmetric bidders.
Journal of Business and Economic Statistics.
(doi:10.1080/07350015.2021.1895813).
Abstract
The paper proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon (2008), this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is in two stage. The asymmetry parameters are estimated from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes (2017). Specification testing procedures are also considered. A timber application reveals that weaker bidders have 30% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer (1996) valid under symmetry.
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Accepted/In Press date: 22 February 2021
Published date: 1 March 2021
Additional Information:
Nathalie Gimenes and Emmanuel Guerre acknowledge the British Academy and Newton Fund for generously funding this project (reference code: AF150085). This project was completed during Jayeeta's PhD from Queen Mary University of London (QMUL) and she thankfully acknowledges generous funding from the School of Economics and Finance, QMUL. Nathalie Gimenes thanks Ying Fan and Ginger Jin for very useful comments. The authors would like to thank three anonymous referees and the Editor Chris Hansen, who made many stimulating suggestions, in particular proposing to address specification issues. Many comments from seminar and conference participants have helped to improve the paper.
Keywords:
Private values, ascending auctions, asymmetry, quantile regression, seller expected revenue, two stage quantile regression estimation
Identifiers
Local EPrints ID: 447978
URI: http://eprints.soton.ac.uk/id/eprint/447978
ISSN: 0735-0015
PURE UUID: b216167e-5fd1-4e2e-9721-0f2194b280b8
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Date deposited: 29 Mar 2021 16:36
Last modified: 17 Mar 2024 06:27
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Author:
Emmanuel Guerre
Author:
Nathalie Gimenes
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